With the increasingly serious energy crisis and the rapid development of urbanization, the integrated design of photovoltaic and building will become an important trend of urban solar energy development. In urban high-density areas, buildings with different heights are densely distributed, which make the distribution of solar radiation on the surface of the building unevenly. Especially, the mutual shielding between buildings has a great impact on the utilization of solar energy. Calculating the available solar radiation on different building surfaces and evaluating the potential of solar energy utilization plays an important role in guiding the installation of distributed photovoltaic energy in urban high-density areas, improving urban energy efficiency and optimizing energy structure.
The traditional evaluation method of solar energy utilization potential mainly relies on human subjective judgment. However, the solar radiation distribution depends on time, climate and the relative spatial position of buildings in the survey area. For the complex and variable survey environment of urban high-density areas, simple investigations and subjective calculations are difficult to cope with. For the traditional manual measurement method, the analysis efficiency is low, and the data acquisition and post-processing are separated, which is difficult to meet the demand.
At present, the evaluation of solar energy utilization potential mainly focuses on airborne LiDAR (Light Detection and Ranging, LiDAR) point cloud data, and most of them are in the research stage. The processing flow is mainly to generate the digital surface model (DSM) from the original point cloud data, and then calculate shadow directly on the DSM grid. However, DSM has obvious characteristics of 2.5 D, which cannot depict the building facade well and cannot complete the calculation of solar radiation illumination of the facade. For the complex environment of high-density urban areas, geometric modeling of the collected data is still needed to ensure the quality and integrity of solar energy utilization potential analysis. At the same time, the LiDAR point cloud data processing process is complex, and the amount of manual interaction is large. The cost of obtaining large area point cloud data is high and difficult to popularize.